1 citations · 2 across the 9 of their papers we have counts for
13 papers
Prospective Forcing: Learning to Look Ahead for Autoregressive Long Video Diffusion
Yifan Shen, Yewei Xia, Gongxu Luo +6
Autoregressive video generators synthesize long videos through chunk-wise rollouts, yet they are typically trained to predict only the immediate future. This limited prediction hor…
Multi-Turn Agentic Scientific Literature Search via Workflow Induction
Jisen Li, Bingxuan Li, Nanyi Jiang +10
Scientific literature search often requires more than retrieving papers from a single query: users' intents are underspecified, preference-dependent, and evolve through interaction…
MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment
Peiyuan Zhu, Shaoan Xie, Zijian Li +5
Contrastive pre-training has propelled video-text alignment, yet models often inherit the critical limitations of their image-text predecessors like CLIP, resulting in entangled re…
Towards Identifiability of Hierarchical Temporal Causal Representation Learning
Zijian Li, Minghao Fu, Junxian Huang +5
Modeling hierarchical latent dynamics behind time series data is critical for capturing temporal dependencies across multiple levels of abstraction in real-world tasks. However, ex…
CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulations
Guangyi Chen, Yunlong Deng, Peiyuan Zhu +4
Causal Representation Learning (CRL) aims to uncover the data-generating process and identify the underlying causal variables and relations, whose evaluation remains inherently cha…
Flow: Modularized Agentic Workflow Automation
Boye Niu, Yiliao Song, Kai Lian +4
Multi-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution. However, the effective adjustment of agenti…
Reflection-Window Decoding: Text Generation with Selective Refinement
Zeyu Tang, Zhenhao Chen, Xiangchen Song +6
The autoregressive decoding for text generation in large language models (LLMs), while widely used, is inherently suboptimal due to the lack of a built-in mechanism to perform refi…
Controllable Video Generation with Provable Disentanglement
Yifan Shen, Peiyuan Zhu, Zijian Li +6
Controllable video generation remains a significant challenge, despite recent advances in generating high-quality and consistent videos. Most existing methods for controlling video…
Continual Learning of Nonlinear Independent Representations
Boyang Sun, Ignavier Ng, Guangyi Chen +3
Identifying the causal relations between interested variables plays a pivotal role in representation learning as it provides deep insights into the dataset. Identifiability, as the…
From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals
Ruichu Cai, Zhifang Jiang, Zijian Li +6
Existing methods for multi-modal time series representation learning aim to disentangle the modality-shared and modality-specific latent variables. Although achieving notable perfo…
On the Identification of Temporally Causal Representation with Instantaneous Dependence
Zijian Li, Yifan Shen, Kaitao Zheng +5
Temporally causal representation learning aims to identify the latent causal process from time series observations, but most methods require the assumption that the latent causal p…
Nonstationary Time Series Forecasting via Unknown Distribution Adaptation
Zijian Li, Ruichu Cai, Zhenhui Yang +6
As environments evolve, temporal distribution shifts can degrade time series forecasting performance. A straightforward solution is to adapt to nonstationary changes while preservi…
CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
Guangyi Chen, Yifan Shen, Zhenhao Chen +5
Identifying the underlying time-delayed latent causal processes in sequential data is vital for grasping temporal dynamics and making downstream reasoning. While some recent method…